Data Augmentation: Scene text detection methods with deep neural networks are data-driven.Data augmentation thus plays significant roles in learning robust models. However, different data augmentation strategies may cause the performance change of the model in complex scenarios. For example, previous methods randomly crop the sub-image from the input image with different sizes (, 512\(\times \) 512 [1]}, 640\(\times \) 640 [2]}, [3]}, [4]}, 768\(\times \) 768 [5]}, 960\(\times \) 960 [6]}, etc.), which may make the model have different generalization abilities to the scales of texts.Meanwhile, some existing methods randomly rotate the input image with different angles (, {0\(^{\circ }\) , 90\(^{\circ }\) , 180\(^{\circ }\) , 270\(^{\circ }\) } [1]}, [-10\(^{\circ }\) , 10\(^{\circ }\) ] [3]}, [4]}, etc.), pursuing the robustness to the rotated arbitrary-shape scene texts.
